Weihe River Water Quality-based Semi-supervised Learning Of Quantitative Remote Sensing Research

Weihe River in Shaanxi Province with fertile land and abundant resources, is an important crop and livestock origin. It is the polity,economy and civilization centre of Shaanxi Province. With the increase of population and the development of industry, the shortage of water resources and water quality pollution are more and more serious, which have destroyed the ecological environment and people's lives and restricted the economic development. Therefore the water quality monitoring of Weihe River in Shaanxi Province has important practical significance. The traditional method of water quality monitoring limited by the human, material, climate and other objective conditions, is difficult to achieve continuous, fast track the investigation and analysis.Utilization of water quality remote sensing image and field water quality data and building water quality monitoring model can monitor the water quality environment completely,fast and dynamically. However a number of field water quality data is hard to get because of objective conditions. So building a semi-supervised remote sensing data's retrieving model of water quality monitoring is an effective method for water quality monitoring by semi-supervised learning theory.This paper is mainly studied the Weihe River in Shaanxi section, summaryizes the principles of remote monitoring of water quality and research regional profiles, analysis of the semi-supervised learning theory, particularly the problem of semi-supervised regression methods, introduced statistical learning theory and support vector machines and other related theoretical knowledge.The main research work of this paper include:(1) Describes two common parameters for the optimization of support vector machines for intelligent optimization algorithm, particle swarm optimization and genetic algorithm. Introduced particle swarm optimization (PSO) of the basic principles, improved the standard PSO-based support vector machine regression model (PSO-SVM) using semi-supervised self-training method. Established the semi-supervised support vector machine regression model (PSSRM) which based on the particle swarm algorithm, and apply it to the Weihe River water quality in quantitative remote sensing inversion then comparing the result to the particle swarm optimization based on support vector machine regression model(PSO-SVM). The regression model improve the regression accuracy, convergence speed, adjustable parameters are few and easy to implement to some extent.(2) Introduced the basic theory and principle about collaborative training algorithm and genetic algorithm, combined the coordinate training algorithm and the SVM regression model based on GA optimization parameter (GA-SVM), Established collaborative training semi-supervised regression model based on GA optimization parameters(GSSRCM),and applied it into the Weihe River water quality in quantitative remote sensing inversion,then compared the regression results and GA-SVM. The model overcomes the PSO model instability, less precise, easy to divergence and other shortcomings, improved the precision and the regression model generalization effectively, which can be make predicted inversion for various types of water quality variables. The results show that Semi-supervised learning based on the regression model is able to achieve the water quality of Weihe River quantitative remote sensing inversion forecast. The GSSRCM regression model was applied to the Weihe River in Shaanxi section of the overall river water quality variable inversion in this paper, the predicted results and the actual situation is consistent,which further evidence of the validity of the model.